{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-10-07T05:02:55.792770Z",
     "start_time": "2025-10-07T05:02:53.987415Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from data_resource.data_bases import engine"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:03:30.123685Z",
     "start_time": "2025-10-07T05:02:55.794288Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 获取后复权行情数据，市场收益率用中证全指000985.CSI替代\n",
    "sql1 = \"\"\"\n",
    "    select a.ticker as code, a.trade_date, a.close, a.open, a.pct_chg/100 as daily_return, b.pct_chg/100 as market_return\n",
    "    from quant_research.market_daily_ts as a\n",
    "    left join quant_research.market_index_daily as b on a.trade_date=b.trade_date\n",
    "    where b.index_code='000985.CSI' and a.trade_date>='2010-01-01' and a.ticker in (\n",
    "        -- 用中证1000, 中证800成分股做为股票池\n",
    "        select con_code from quant_research.index_constituent where trade_date=(\n",
    "            select max(trade_date) from quant_research.index_constituent\n",
    "        ) and index_code in ('000852.SH', '000906.SH')\n",
    "    )\n",
    "    order by a.trade_date;\n",
    "\"\"\"\n",
    "raw = pd.read_sql(sql1, engine)\n",
    "\n",
    "# 获取市场月度收益率\n",
    "sql1 = \"\"\"\n",
    "    select trade_date, pct_chg/100 as market_daily_return\n",
    "    from quant_research.market_index_daily\n",
    "    where index_code='000985.CSI' and trade_date>='2009-01-01'\n",
    "    order by trade_date;\n",
    "\"\"\"\n",
    "market_data = pd.read_sql(sql1, engine)\n",
    "market_data['trade_date'] = pd.to_datetime(market_data['trade_date'])\n",
    "market_data['month'] = market_data['trade_date'].dt.to_period('M')\n",
    "market_data['market_return_month'] = market_data.groupby('month')['market_daily_return'].transform(lambda x: np.sum(np.log(1+x)))\n",
    "market_data1 = market_data[['month', 'market_return_month']].drop_duplicates()\n",
    "market_data1.reset_index(inplace=True,drop=True)\n",
    "# 延迟1期\n",
    "market_data1['market_return_month_lag'] = market_data1['market_return_month'].shift(1)\n",
    "market_data1.dropna(inplace=True)"
   ],
   "id": "f7dc075d5038b9b1",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:03:31.866506Z",
     "start_time": "2025-10-07T05:03:30.124693Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 给上月频、周频标签\n",
    "raw1 = raw.copy()\n",
    "raw1['trade_date'] = pd.to_datetime(raw1['trade_date'])\n",
    "raw1['month'] = raw1['trade_date'].dt.to_period('M')\n",
    "raw1['week'] = raw1['trade_date'].dt.to_period('W')"
   ],
   "id": "655f703f41a1a55b",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:03:50.932941Z",
     "start_time": "2025-10-07T05:03:31.867562Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 按个股和月份分组，筛选数据量不少于15个交易日的记录\n",
    "grouped = raw1.groupby(['code', 'month'])\n",
    "filtered_data = grouped.filter(lambda x: len(x) >= 15)"
   ],
   "id": "19b894e8a5c8990b",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:04:32.082282Z",
     "start_time": "2025-10-07T05:03:50.934951Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算月度CAPM残差方差\n",
    "def iv(data):\n",
    "    # X = sm.add_constant(data['market_return'])  # 加常数项\n",
    "    # y = data[\"daily_return\"]\n",
    "    # model = sm.OLS(y, X).fit()\n",
    "    \n",
    "    # 用数值计算获取CAPM残差\n",
    "    x = data['market_return'].values\n",
    "    y = data['daily_return'].values\n",
    "    \n",
    "    # 剔除na值\n",
    "    mask = ~(np.isnan(x) | np.isnan(y))\n",
    "    x = x[mask]\n",
    "    y = y[mask]\n",
    "    \n",
    "    x_mean = np.mean(x)\n",
    "    y_mean = np.mean(y)\n",
    "    \n",
    "    # beta = cov(x,y)/var(x)\n",
    "    numerator = np.sum((x- x_mean) * (y-y_mean))\n",
    "    denominator = np.sum((x-x_mean)**2)\n",
    "    if denominator == 0:\n",
    "        return np.nan\n",
    "    beta = numerator / denominator\n",
    "    alpha = y_mean - beta * x_mean\n",
    "    residuals = y - (alpha + beta * x)\n",
    "    return np.var(residuals)\n",
    "    \n",
    "iv_month = filtered_data.groupby(['code', 'month'])[['daily_return', 'market_return']].apply(lambda x: iv(x))\n",
    "iv_month.name='iv_month'\n",
    "iv_month = iv_month.to_frame()\n",
    "iv_month['iv_lag'] = iv_month.groupby('code')['iv_month'].shift(1)\n",
    "iv_month.reset_index(inplace=True)\n",
    "iv_month.dropna(inplace=True)"
   ],
   "id": "3c1e79cce0f9c67c",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:04:32.088241Z",
     "start_time": "2025-10-07T05:04:32.083292Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 日度：正负收益最大持续天数\n",
    "def get_streak(series: pd.Series, count_type: str = 'positive', mode: str = 'single', window: int = 12):\n",
    "    \"\"\"\n",
    "    统一版本：计算连续正/负收益长度（支持单序列或滚动模式）\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    series : pd.Series\n",
    "        输入序列（如 daily_return）。\n",
    "    count_type : {'positive', 'negative'}\n",
    "        连续方向类型。\n",
    "    mode : {'single', 'rolling'}\n",
    "        'single' 模式 -> 返回整个序列中最长连续长度（标量）。\n",
    "        'rolling' 模式 -> 返回每个窗口内的最大连续长度（Series）。\n",
    "    window : int, optional\n",
    "        滚动窗口长度，仅在 mode='rolling' 时生效。\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result : int or pd.Series\n",
    "        - 若 mode='single'：返回最大连续长度（int）\n",
    "        - 若 mode='rolling'：返回与输入等长的 Series\n",
    "    \"\"\"\n",
    "    arr = series.to_numpy()\n",
    "    if len(arr) == 0:\n",
    "        return np.nan if mode == 'rolling' else 0\n",
    "\n",
    "    sign = 1 if count_type == 'positive' else -1\n",
    "    _mask = np.sign(arr) == sign\n",
    "    _mask = np.where(np.isnan(arr), False, _mask)\n",
    "\n",
    "    def max_run(m):\n",
    "        m = np.concatenate(([False], m, [False]))\n",
    "        diff = np.diff(m.astype(int))\n",
    "        starts = np.flatnonzero(diff == 1)\n",
    "        ends = np.flatnonzero(diff == -1)\n",
    "        return (ends - starts).max() if len(starts) else 0\n",
    "\n",
    "    if mode == 'single':\n",
    "        return max_run(_mask)\n",
    "\n",
    "    elif mode == 'rolling':\n",
    "        return pd.Series(_mask, index=series.index).rolling(window).apply(max_run, raw=True)\n",
    "\n",
    "    else:\n",
    "        raise ValueError(\"mode must be 'single' or 'rolling'.\")\n"
   ],
   "id": "286a761dae9e218f",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:12:23.041534Z",
     "start_time": "2025-10-07T05:04:32.089248Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算周度、月度涨跌幅\n",
    "filtered_data_week = filtered_data.groupby(['code', 'week'])['daily_return'].apply(lambda x: np.sum(np.log(1+x)))\n",
    "filtered_data_week.name = 'daily_return_week'\n",
    "filtered_data_week = filtered_data_week.to_frame()\n",
    "filtered_data_week.reset_index(inplace=True)\n",
    "\n",
    "filtered_data_month = filtered_data.groupby(['code', 'month'])['daily_return'].apply(lambda x: np.sum(np.log(1+x)))\n",
    "filtered_data_month.name = 'daily_return_month'\n",
    "filtered_data_month = filtered_data_month.to_frame()\n",
    "filtered_data_month.reset_index(inplace=True)\n",
    "\n",
    "# 左连接至原始数据\n",
    "filtered_data = filtered_data.merge(filtered_data_week, on=['code', 'week'], how='left')\n",
    "filtered_data = filtered_data.merge(filtered_data_month, on=['code', 'month'], how='left')"
   ],
   "id": "a5293f1e6bae194d",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:15:54.637189Z",
     "start_time": "2025-10-07T05:12:23.042582Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 日频：正负收益最大持续天数\n",
    "pd_daily = filtered_data.groupby(['code', 'month'])['daily_return'].apply(lambda x: get_streak(x, count_type='positive', mode='single'))\n",
    "pd_daily.name = 'pd_d'\n",
    "pd_daily = pd_daily.reset_index()\n",
    "pn_daily = filtered_data.groupby(['code', 'month'])['daily_return'].apply(lambda x: get_streak(x, count_type='negative', mode='single'))\n",
    "pn_daily.name = 'pn_d'\n",
    "pn_daily = pn_daily.reset_index()\n",
    "\n",
    "# 周频：正负收益最大持续周数，截断为最大12周\n",
    "filtered_data_week['pd_w'] = filtered_data_week.groupby('code')['daily_return_week'].transform(lambda x: get_streak(x, count_type='positive', mode='rolling', window=12))\n",
    "filtered_data_week['pn_w'] = filtered_data_week.groupby('code')['daily_return_week'].transform(lambda x: get_streak(x, count_type='negative', mode='rolling', window=12))\n",
    "\n",
    "# 月频：正负收益最大持续月数，截断为最大12月\n",
    "filtered_data_month['pd_m'] = filtered_data_month.groupby('code')['daily_return_month'].transform(lambda x: get_streak(x, count_type='positive', mode='rolling', window=12))\n",
    "filtered_data_month['pn_m'] = filtered_data_month.groupby('code')['daily_return_month'].transform(lambda x: get_streak(x, count_type='negative', mode='rolling', window=12))"
   ],
   "id": "add3563efb6e79c6",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:16:25.668340Z",
     "start_time": "2025-10-07T05:15:54.638788Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将所有特征合并\n",
    "raw_features = filtered_data.copy()\n",
    "raw_features = raw_features.merge(iv_month, on=['code', 'month'], how='left')\n",
    "raw_features = raw_features.merge(pd_daily, on=['code', 'month'], how='left')\n",
    "raw_features = raw_features.merge(pn_daily, on=['code', 'month'], how='left')\n",
    "raw_features = raw_features.merge(filtered_data_week[['code', 'week', 'pd_w', 'pn_w']], on=['code', 'week'], how='left')\n",
    "raw_features = raw_features.merge(filtered_data_month[['code', 'month', 'pd_m', 'pn_m']], on=['code', 'month'], how='left')\n",
    "raw_features = raw_features.merge(market_data1[['month', 'market_return_month_lag']], on=['month'], how='left')\n",
    "\n",
    "# 对特征进行滞后1期处理\n",
    "raw_features['pd_d_lag'] = raw_features.groupby('code')['pd_d'].shift(1)\n",
    "raw_features['pn_d_lag'] = raw_features.groupby('code')['pn_d'].shift(1)\n",
    "raw_features['pd_w_lag'] = raw_features.groupby('code')['pd_w'].shift(1)\n",
    "raw_features['pn_w_lag'] = raw_features.groupby('code')['pn_w'].shift(1)\n",
    "raw_features['pd_m_lag'] = raw_features.groupby('code')['pd_m'].shift(1)\n",
    "raw_features['pn_m_lag'] = raw_features.groupby('code')['pn_m'].shift(1)\n",
    "raw_features['daily_return_month_lag'] = raw_features.groupby('code')['daily_return_month'].shift(1)\n",
    "\n",
    "raw_features.dropna(inplace=True)\n",
    "\n",
    "# 将每月最后一日保留为信号集\n",
    "monthly_features = raw_features.groupby(['code', 'month']).tail(1)\n",
    "monthly_features1 = monthly_features[['code', 'month', 'close', 'open', 'daily_return_month', 'daily_return_month_lag', 'market_return_month_lag',\n",
    "                                      'iv_lag', 'pd_d_lag', 'pn_d_lag', 'pd_w_lag', 'pn_w_lag', 'pd_m_lag', 'pn_m_lag']].copy()\n",
    "monthly_features1.reset_index(inplace=True,drop=True)\n",
    "\n",
    "# 生成方向信号\n",
    "monthly_features1['direction'] = np.where(monthly_features1['daily_return_month'] > 0, 1, -1)"
   ],
   "id": "c10f0eec31bb6f2a",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:16:25.898356Z",
     "start_time": "2025-10-07T05:16:25.669373Z"
    }
   },
   "cell_type": "code",
   "source": "monthly_features1.describe()",
   "id": "837844af53a26830",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               close           open  daily_return_month  \\\n",
       "count  548453.000000  548453.000000       548453.000000   \n",
       "mean      111.500009     111.192247            0.002150   \n",
       "std      1040.730858    1037.497282            0.128595   \n",
       "min         0.780000       0.780000           -1.499677   \n",
       "25%        20.130000      20.070000           -0.068515   \n",
       "50%        36.160000      36.040000           -0.002273   \n",
       "75%        68.490000      68.240000            0.067153   \n",
       "max    119197.100000  108708.680000            2.470841   \n",
       "\n",
       "       daily_return_month_lag  market_return_month_lag        iv_lag  \\\n",
       "count           548453.000000            548453.000000  5.484530e+05   \n",
       "mean                 0.002150                 0.002845  5.575138e-04   \n",
       "std                  0.128595                 0.061216  9.997594e-04   \n",
       "min                 -1.499677                -0.297159  3.997828e-08   \n",
       "25%                 -0.068515                -0.029751  1.573537e-04   \n",
       "50%                 -0.002273                 0.000896  3.233229e-04   \n",
       "75%                  0.067153                 0.028077  6.847085e-04   \n",
       "max                  2.470841                 0.199696  5.172163e-01   \n",
       "\n",
       "            pd_d_lag       pn_d_lag       pd_w_lag       pn_w_lag  \\\n",
       "count  548453.000000  548453.000000  548453.000000  548453.000000   \n",
       "mean        3.498493       3.519988       2.960336       3.062933   \n",
       "std         1.435954       1.433407       1.388764       1.394843   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         2.000000       3.000000       2.000000       2.000000   \n",
       "50%         3.000000       3.000000       3.000000       3.000000   \n",
       "75%         4.000000       4.000000       4.000000       4.000000   \n",
       "max        21.000000      22.000000      12.000000      12.000000   \n",
       "\n",
       "            pd_m_lag       pn_m_lag      direction  \n",
       "count  548453.000000  548453.000000  548453.000000  \n",
       "mean        2.836544       3.011698      -0.018497  \n",
       "std         1.314002       1.322061       0.999830  \n",
       "min         0.000000       0.000000      -1.000000  \n",
       "25%         2.000000       2.000000      -1.000000  \n",
       "50%         3.000000       3.000000      -1.000000  \n",
       "75%         3.000000       4.000000       1.000000  \n",
       "max        12.000000      12.000000       1.000000  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>daily_return_month</th>\n",
       "      <th>daily_return_month_lag</th>\n",
       "      <th>market_return_month_lag</th>\n",
       "      <th>iv_lag</th>\n",
       "      <th>pd_d_lag</th>\n",
       "      <th>pn_d_lag</th>\n",
       "      <th>pd_w_lag</th>\n",
       "      <th>pn_w_lag</th>\n",
       "      <th>pd_m_lag</th>\n",
       "      <th>pn_m_lag</th>\n",
       "      <th>direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>548453.000000</td>\n",
       "      <td>548453.000000</td>\n",
       "      <td>548453.000000</td>\n",
       "      <td>548453.000000</td>\n",
       "      <td>548453.000000</td>\n",
       "      <td>5.484530e+05</td>\n",
       "      <td>548453.000000</td>\n",
       "      <td>548453.000000</td>\n",
       "      <td>548453.000000</td>\n",
       "      <td>548453.000000</td>\n",
       "      <td>548453.000000</td>\n",
       "      <td>548453.000000</td>\n",
       "      <td>548453.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>111.500009</td>\n",
       "      <td>111.192247</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>0.002845</td>\n",
       "      <td>5.575138e-04</td>\n",
       "      <td>3.498493</td>\n",
       "      <td>3.519988</td>\n",
       "      <td>2.960336</td>\n",
       "      <td>3.062933</td>\n",
       "      <td>2.836544</td>\n",
       "      <td>3.011698</td>\n",
       "      <td>-0.018497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1040.730858</td>\n",
       "      <td>1037.497282</td>\n",
       "      <td>0.128595</td>\n",
       "      <td>0.128595</td>\n",
       "      <td>0.061216</td>\n",
       "      <td>9.997594e-04</td>\n",
       "      <td>1.435954</td>\n",
       "      <td>1.433407</td>\n",
       "      <td>1.388764</td>\n",
       "      <td>1.394843</td>\n",
       "      <td>1.314002</td>\n",
       "      <td>1.322061</td>\n",
       "      <td>0.999830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>-1.499677</td>\n",
       "      <td>-1.499677</td>\n",
       "      <td>-0.297159</td>\n",
       "      <td>3.997828e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.130000</td>\n",
       "      <td>20.070000</td>\n",
       "      <td>-0.068515</td>\n",
       "      <td>-0.068515</td>\n",
       "      <td>-0.029751</td>\n",
       "      <td>1.573537e-04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.160000</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>-0.002273</td>\n",
       "      <td>-0.002273</td>\n",
       "      <td>0.000896</td>\n",
       "      <td>3.233229e-04</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>68.490000</td>\n",
       "      <td>68.240000</td>\n",
       "      <td>0.067153</td>\n",
       "      <td>0.067153</td>\n",
       "      <td>0.028077</td>\n",
       "      <td>6.847085e-04</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>119197.100000</td>\n",
       "      <td>108708.680000</td>\n",
       "      <td>2.470841</td>\n",
       "      <td>2.470841</td>\n",
       "      <td>0.199696</td>\n",
       "      <td>5.172163e-01</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:16:47.640507Z",
     "start_time": "2025-10-07T05:16:25.899434Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tqdm import tqdm\n",
    "\n",
    "\"\"\"\n",
    "构建线性概率模型，月度滚动预测\n",
    "因子值为预测概率值：1-正收益；-1-负收益\n",
    "\"\"\"\n",
    "train_window = 36\n",
    "# 样本外时间点：从第train_window+1期开始\n",
    "oos_dates = monthly_features1['month'].unique()[train_window:]\n",
    "\n",
    "# 定义滚动函数\n",
    "def rolling_regression(train_data, test_data, feature_cols, target_col):\n",
    "    \"\"\"\n",
    "    用训练集拟合线性回归，预测测试集的因子值\n",
    "    feature_cols: 特征列名称, list\n",
    "    target_col: 自变量列名称\n",
    "    \"\"\"\n",
    "    # 提取特征和目标变量\n",
    "    X_train = train_data[feature_cols].values\n",
    "    y_train = train_data[target_col].values\n",
    "    X_test = test_data[feature_cols].values\n",
    "    \n",
    "    # 标准化特征\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "    \n",
    "    # 拟合线性回归\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_train_scaled, y_train)\n",
    "    \n",
    "    # 预测因子值（即模型对测试集的预测结果）\n",
    "    test_data['factor_value'] = model.predict(X_test_scaled)\n",
    "    \n",
    "    return test_data[['code', 'month', 'factor_value', 'close', 'open']]\n",
    "\n",
    "\"\"\"样本外滚动回测\"\"\"\n",
    "# 定义特征列和目标列\n",
    "feature_cols = ['daily_return_month_lag', 'market_return_month_lag', 'iv_lag', 'pd_d_lag', 'pn_d_lag', 'pd_w_lag', 'pn_w_lag', 'pd_m_lag', 'pn_m_lag']\n",
    "target_col = 'direction'\n",
    "\n",
    "# 存储所有样本外预测结果\n",
    "all_factor_results = []\n",
    "\n",
    "# 遍历每个样本外时间点，执行滚动回归\n",
    "for oos_date in tqdm(oos_dates, desc=\"滚动回归进度\"):\n",
    "    # 1. 定义训练集：所有时间 < 当前样本外时间的数据，且为有效样本（可加筛选条件）\n",
    "    train_data = monthly_features1[\n",
    "        (monthly_features1['month'] < oos_date) &  # 只使用历史数据\n",
    "        (monthly_features1[feature_cols].notna().all(axis=1)) &  # 特征无缺失\n",
    "        (monthly_features1[target_col].notna())  # 目标变量无缺失\n",
    "    ]\n",
    "    \n",
    "    # 2. 定义测试集：当前样本外时间的数据（需预测的因子值）\n",
    "    test_data = monthly_features1[\n",
    "        (monthly_features1['month'] == oos_date) &  # 仅当前时间点\n",
    "        (monthly_features1[feature_cols].notna().all(axis=1))  # 特征无缺失（确保可预测）\n",
    "    ].copy()\n",
    "    \n",
    "    # 3. 跳过训练集或测试集为空的情况\n",
    "    if len(train_data) == 0 or len(test_data) == 0:\n",
    "        continue\n",
    "    \n",
    "    # 4. 用训练集拟合模型，预测测试集因子值\n",
    "    oos_result = rolling_regression(\n",
    "        train_data=train_data,\n",
    "        test_data=test_data,\n",
    "        feature_cols=feature_cols,\n",
    "        target_col=target_col\n",
    "    )\n",
    "    \n",
    "    # 5. 保存当前时间点的预测结果\n",
    "    all_factor_results.append(oos_result)\n",
    "\n",
    "factors = pd.concat(all_factor_results)\n",
    "factors.reset_index(inplace=True, drop=True)"
   ],
   "id": "cafc7046b973565b",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "滚动回归进度: 100%|██████████| 142/142 [00:20<00:00,  7.09it/s]\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:16:47.690961Z",
     "start_time": "2025-10-07T05:16:47.641512Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将月末交易日左连接至信号值\n",
    "sql2 = \"\"\"\n",
    "    select cal_date as trading\n",
    "    from quant_research.basic_trading_date\n",
    "    where is_open=1 and cal_date >= '2010-01-01'\n",
    "    order by cal_date\n",
    "\"\"\"\n",
    "trade_cal = pd.read_sql(sql2, engine)\n",
    "trade_cal['trading'] = pd.to_datetime(trade_cal['trading'])\n",
    "trade_cal['month'] = trade_cal['trading'].dt.to_period('M')\n",
    "trade_cal1 = trade_cal.groupby('month').tail(1)\n",
    "trade_cal1.reset_index(inplace=True, drop=True)\n",
    "\n",
    "factors = factors.merge(trade_cal1, on=['month'], how='left')"
   ],
   "id": "c86cd3e2d1bdab5f",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:16:47.735036Z",
     "start_time": "2025-10-07T05:16:47.692661Z"
    }
   },
   "cell_type": "code",
   "source": "factors[factors['code'] == '002166']",
   "id": "20f3bee7ca564f7e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          code    month  factor_value  close   open    trading\n",
       "2       002166  2013-12      1.602927  38.72  35.54 2013-12-31\n",
       "3735    002166  2014-01      0.009744  38.54  37.64 2014-01-30\n",
       "4756    002166  2014-02     -0.865300  33.65  33.01 2014-02-28\n",
       "7052    002166  2014-03     -0.236069  32.53  32.31 2014-03-31\n",
       "8748    002166  2014-04     -0.627701  29.69  29.49 2014-04-30\n",
       "...        ...      ...           ...    ...    ...        ...\n",
       "456717  002166  2025-05      0.423949  59.16  59.23 2025-05-30\n",
       "466041  002166  2025-06     -0.245865  58.77  59.07 2025-06-30\n",
       "466801  002166  2025-07      0.335438  62.79  62.34 2025-07-31\n",
       "471854  002166  2025-08     -0.147674  60.51  61.50 2025-08-29\n",
       "479945  002166  2025-09     -0.304271  58.62  58.46 2025-09-30\n",
       "\n",
       "[138 rows x 6 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>month</th>\n",
       "      <th>factor_value</th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>trading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>002166</td>\n",
       "      <td>2013-12</td>\n",
       "      <td>1.602927</td>\n",
       "      <td>38.72</td>\n",
       "      <td>35.54</td>\n",
       "      <td>2013-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3735</th>\n",
       "      <td>002166</td>\n",
       "      <td>2014-01</td>\n",
       "      <td>0.009744</td>\n",
       "      <td>38.54</td>\n",
       "      <td>37.64</td>\n",
       "      <td>2014-01-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4756</th>\n",
       "      <td>002166</td>\n",
       "      <td>2014-02</td>\n",
       "      <td>-0.865300</td>\n",
       "      <td>33.65</td>\n",
       "      <td>33.01</td>\n",
       "      <td>2014-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7052</th>\n",
       "      <td>002166</td>\n",
       "      <td>2014-03</td>\n",
       "      <td>-0.236069</td>\n",
       "      <td>32.53</td>\n",
       "      <td>32.31</td>\n",
       "      <td>2014-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8748</th>\n",
       "      <td>002166</td>\n",
       "      <td>2014-04</td>\n",
       "      <td>-0.627701</td>\n",
       "      <td>29.69</td>\n",
       "      <td>29.49</td>\n",
       "      <td>2014-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456717</th>\n",
       "      <td>002166</td>\n",
       "      <td>2025-05</td>\n",
       "      <td>0.423949</td>\n",
       "      <td>59.16</td>\n",
       "      <td>59.23</td>\n",
       "      <td>2025-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466041</th>\n",
       "      <td>002166</td>\n",
       "      <td>2025-06</td>\n",
       "      <td>-0.245865</td>\n",
       "      <td>58.77</td>\n",
       "      <td>59.07</td>\n",
       "      <td>2025-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466801</th>\n",
       "      <td>002166</td>\n",
       "      <td>2025-07</td>\n",
       "      <td>0.335438</td>\n",
       "      <td>62.79</td>\n",
       "      <td>62.34</td>\n",
       "      <td>2025-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471854</th>\n",
       "      <td>002166</td>\n",
       "      <td>2025-08</td>\n",
       "      <td>-0.147674</td>\n",
       "      <td>60.51</td>\n",
       "      <td>61.50</td>\n",
       "      <td>2025-08-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479945</th>\n",
       "      <td>002166</td>\n",
       "      <td>2025-09</td>\n",
       "      <td>-0.304271</td>\n",
       "      <td>58.62</td>\n",
       "      <td>58.46</td>\n",
       "      <td>2025-09-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 6 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-07T05:16:59.265476Z",
     "start_time": "2025-10-07T05:16:47.737629Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\"\"\"\n",
    "因子检验\n",
    "\"\"\"\n",
    "from factorAnaly.factorEngine import FactorEngine\n",
    "\n",
    "f_dMOM= FactorEngine(factor_raw=factors, factor_name='factor_value', rebalance_period=20)  # 默认：20交易日调仓周期\n",
    "\n",
    "f_dMOM.run(signal_name='factor_value')\n",
    "f_dMOM.plotting_ic(factor_name='factor_value')\n",
    "f_dMOM.plotting_group_return(factor_name='factor_value')"
   ],
   "id": "df040967dcd6a0b5",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
